That's an example simplified hugely and making a number of assumptions, but with that amount of people you can be 95% certain that the true percentage will be within the range. we would not be able to claim the original study was flawed) despite the fact that it's a much larger population. 43% falls within these ranges, so we fail to reject the null hypothesis (i.e. ![]() Therefore the range that the true percentage is, if the study is accurate (36-11.47)% <= p <= (36+11.47)% (I'm not doing these calculations. Original study found 36% would with a sample size of 73. H0 - We do not prove the original study wrong. In it, 431 respondents say they would 'force a woman to have sex' if they'd get away with it. Let's say there was another study, where 1,000 people were sampled from the population. Original Claim: 36% of men would 'force a woman to have sex' if they'd get away with it. 98 divided by the square root of the (sample size) (i.e. A rough way of checking the margin of error for a large population, so long as the sample is a small percentage of the overall population (I know I'm being vague but I don't care to explain at the moment) is. At 1,000,000,000 people is approximately the same, at 100,000 people it's approximately the same, at 1,000 people it's approximately 11.05%. ![]() As a simple demonstration of how irrelevant it is, the margin of error for 1,000,000,000,000 people is approximately 11.47%. The population size, if large, is rather irrelevant. Regarding bolded, yes, and no, It depends more on how precise you want your confidence interval to be than the actual population size which is largely irrelevant unless you've a small population/are sampling a high percentage of the population.Īt 73 people, for a large population, at the 95% confidence interval, your margin of error is going to be approximately 11.47%.
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